Welcome back to Machine Learning Guide. I'm your host, Tyler Rinelli.

MLG teaches the fundamentals of machine learning and artificial intelligence.

It covers intuition, models, math, languages, frameworks, and more.

Where your other machine learning resources provide the trees, I provide the forest.

Visual is the best primary learning modality, but audio is a great supplement during exercise commute and chores.

Consider MLG your syllabus with highly curated resources for each episode's details at OCdevel.com forward slash MLG.

I'm also starting a new podcast which could use your support. It's called

Lefnear's Life Hacks and teaches productivity focused tips and tricks, some which could prove

beneficial in your machine learning education journey. Find that at Ocdevel.com forward slash

LLH. Welcome back to Machine Learning Applied.

In this episode, I'm going to be interviewing co-workers from Dept.

Matt is an expert in architecture, and Girowat is an expert in DevOps or developer operations.

These two skills combine in the deploying of a full-fledged product that can be used by consumers,

a web app, a mobile app. Most of what we've talked about in this podcast series is machine

learning, how to develop and train your model inside of a Docker container, or even developing

and training your models on the

cloud by way of AWS SageMaker's studio notebooks. Now, our skill sets are on the machine learning

side, but eventually you want to get that model into the hands of a customer. After you have

trained your model, whether on local host or on SageMaker,

you will deploy your model through SageMaker to a SageMaker rest endpoint or to a model registry

that can be called as SageMaker batch transform jobs or Sage Sagemaker serverless inference jobs.

So now you have your deployed machine learning model ready to be used, but who's going to

...